The Optimal Ranking Tradeoffs Between Two Ranking Scores in Two-Class Classification
The accuracy, true negative rate (negative class recall), true positive rate (positive class recall), negative predictive value (negative class precision), positive predictive value (positive class precision), class-specific intersections over unions, class-specific F-scores, Cohen's kappa, and balanced accuracy are scores that one intuitively wants to maximize when developing machine learning methods. However, they lead to different rankings of such methods. So, how can we establish a ranking that is an optimal tradeoff between any pair of these scores? It turns out that all these scores are special cases of ranking scores: a continuum of scores parameterized by the relative importance v...
| Authors | Sébastien Piérard, Adrien Deliège, Marc Van Droogenbroeck |
|---|---|
| Primary category | cs.PF |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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